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Yinlong Qian
Researcher at Chinese Academy of Sciences
Publications - 9
Citations - 805
Yinlong Qian is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Steganalysis & Steganography. The author has an hindex of 7, co-authored 9 publications receiving 655 citations. Previous affiliations of Yinlong Qian include University of Science and Technology of China.
Papers
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Proceedings ArticleDOI
Deep learning for steganalysis via convolutional neural networks
TL;DR: A new paradigm for steganalysis to learn features automatically via deep learning models through a customized Convolutional Neural Network that achieves comparable performance on BOSSbase and the realistic and large ImageNet database.
Proceedings ArticleDOI
Learning and transferring representations for image steganalysis using convolutional neural network
TL;DR: It is shown that feature representations learned with a pre-trained CNN for detecting a steganographic algorithm with a high payload can be efficiently transferred to improve the learning of features for detecting the same steganographs with a low pay-load.
Book ChapterDOI
SSGAN: Secure Steganography Based on Generative Adversarial Networks
TL;DR: A novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography with significant improvements made on the convergence speed, the training stability and the image quality.
Posted Content
SSGAN: Secure Steganography Based on Generative Adversarial Networks
TL;DR: In this paper, a novel strategy of secure steganograpy based on Generative Adversarial Networks (GANs) is proposed to generate suitable and secure covers for steganography.
Journal ArticleDOI
Feature learning for steganalysis using convolutional neural networks
TL;DR: This paper proposes a new paradigm for steganalysis based on the concept of feature learning and uses model combination to boost the performance of CNN based method and provides quantitative analysis of the learned features from convolutional layers.